🎨 Palette: Improve CLI UX and Accessibility

.Jules/palette.md

@@ -1,3 +1,7 @@
 ## 2024-05-22 - Visual Hierarchy in CLI Output
 **Learning:** Adding color-coded indicators (Green/Red) and emojis (💰, 📉) in CLI tools significantly reduces cognitive load when parsing financial data streams. It transforms a wall of text into a scannable narrative.
 **Action:** For data-heavy CLI applications, always implement a semantic color system and visual anchors (icons/emojis) for key events.
+
+## 2024-05-23 - Accessibility in CLI Tools
+**Learning:** Command-line tools with hardcoded colors can be inaccessible or noisy in certain terminals/logs. Offering a `--no-color` and `--quiet` flag respects user preference and environment constraints.
+**Action:** Always include standard `argparse` flags for verbosity and color control in CLI utilities.

.gitignore

@@ -39,3 +39,7 @@
 
 # debug information files
 *.dwo
+
+# Python
+__pycache__/
+*.pyc

README.md

@@ -26,10 +26,25 @@ Run the simulation script:
 python bitcoin_trading_simulation.py
 ```
 
+### Options
+
+- `--days`: Number of days to simulate (default: 60)
+- `--initial-cash`: Initial cash amount (default: 10000)
+- `--initial-price`: Initial Bitcoin price (default: 50000)
+- `--volatility`: Volatility factor (default: 0.02)
+- `--quiet`: Suppress daily ledger output
+- `--no-color`: Disable colored output
+
+Example:
+
+```bash
+python bitcoin_trading_simulation.py --days 100 --initial-cash 5000 --quiet --no-color
+```
+
 ## Tests
 
 Run the test suite:
 
 ```bash
-python test.py
+pytest
 ```

bitcoin_trading_simulation.py

@@ -1,6 +1,8 @@
+import argparse
 import numpy as np
 import pandas as pd
 
+
 class Colors:
     HEADER = '\033[95m'
     BLUE = '\033[94m'
@@ -9,6 +11,16 @@ class Colors:
     ENDC = '\033[0m'
     BOLD = '\033[1m'
 
+    @classmethod
+    def disable(cls):
+        cls.HEADER = ''
+        cls.BLUE = ''
+        cls.GREEN = ''
+        cls.RED = ''
+        cls.ENDC = ''
+        cls.BOLD = ''
+
+
 def simulate_bitcoin_prices(days=60, initial_price=50000, volatility=0.02):
     """
     Simulates Bitcoin prices for a given number of days using Geometric Brownian Motion.
@@ -23,6 +35,7 @@ def simulate_bitcoin_prices(days=60, initial_price=50000, volatility=0.02):
         prices.append(prices[-1] + price_change)
     return pd.Series(prices, name='Price')
 
+
 def calculate_moving_averages(prices, short_window=7, long_window=30):
     """
     Calculates short and long moving averages for a given price series.
@@ -33,6 +46,7 @@ def calculate_moving_averages(prices, short_window=7, long_window=30):
     signals['long_mavg'] = prices.rolling(window=long_window, min_periods=1, center=False).mean()
     return signals
 
+
 def generate_trading_signals(signals):
     """
     Generates trading signals based on the Golden Cross strategy.
@@ -44,12 +58,13 @@ def generate_trading_signals(signals):
     signals.loc[signals['short_mavg'] > signals['long_mavg'], 'signal'] = 1.0
     # A Death Cross (sell signal)
     signals.loc[signals['short_mavg'] < signals['long_mavg'], 'signal'] = -1.0
-    
+
     # We create 'positions' to represent the trading action: 1 for buy, -1 for sell, 0 for hold
     signals['positions'] = signals['signal'].diff().shift(1)
     return signals
 
-def simulate_trading(signals, initial_cash=10000):
+
+def simulate_trading(signals, initial_cash=10000, quiet=False):
     """
     Simulates trading based on signals and prints a daily ledger.
     """
@@ -59,7 +74,8 @@ def simulate_trading(signals, initial_cash=10000):
     portfolio['btc'] = 0.0
     portfolio['total_value'] = float(initial_cash)
 
-    print(f"{Colors.HEADER}{Colors.BOLD}------ Daily Trading Ledger ------{Colors.ENDC}")
+    if not quiet:
+        print(f"{Colors.HEADER}{Colors.BOLD}------ Daily Trading Ledger ------{Colors.ENDC}")
     for i, row in signals.iterrows():
         if i > 0:
             portfolio.loc[i, 'cash'] = portfolio.loc[i-1, 'cash']
@@ -81,32 +97,49 @@ def simulate_trading(signals, initial_cash=10000):
                 portfolio.loc[i, 'btc'] = 0
 
         portfolio.loc[i, 'total_value'] = portfolio.loc[i, 'cash'] + portfolio.loc[i, 'btc'] * row['price']
-        print(f"Day {i}: Portfolio Value: ${portfolio.loc[i, 'total_value']:.2f}, Cash: ${portfolio.loc[i, 'cash']:.2f}, BTC: {portfolio.loc[i, 'btc']:.4f}")
-
+        if not quiet:
+            print(
+                f"Day {i}: Portfolio Value: ${portfolio.loc[i, 'total_value']:.2f}, "
+                f"Cash: ${portfolio.loc[i, 'cash']:.2f}, BTC: {portfolio.loc[i, 'btc']:.4f}"
+            )
+
     return portfolio
 
+
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Bitcoin Trading Simulation')
+    parser.add_argument('--days', type=int, default=60, help='Number of days to simulate')
+    parser.add_argument('--initial-cash', type=float, default=10000, help='Initial cash amount')
+    parser.add_argument('--initial-price', type=float, default=50000, help='Initial Bitcoin price')
+    parser.add_argument('--volatility', type=float, default=0.02, help='Volatility factor')
+    parser.add_argument('--quiet', action='store_true', help='Suppress daily ledger output')
+    parser.add_argument('--no-color', action='store_true', help='Disable colored output')
+    args = parser.parse_args()
+
+    if args.no_color:
+        Colors.disable()
+
     # Simulate prices
-    prices = simulate_bitcoin_prices()
-    
+    prices = simulate_bitcoin_prices(days=args.days, initial_price=args.initial_price, volatility=args.volatility)
+
     # Calculate moving averages
     signals = calculate_moving_averages(prices)
-    
+
     # Generate trading signals
     signals = generate_trading_signals(signals)
-    
+
     # Simulate trading
-    portfolio = simulate_trading(signals)
-    
+    portfolio = simulate_trading(signals, initial_cash=args.initial_cash, quiet=args.quiet)
+
     # Final portfolio performance
     final_value = portfolio['total_value'].iloc[-1]
-    initial_cash = 10000
+    initial_cash = args.initial_cash
     profit = final_value - initial_cash
-    
+
     # Compare with buy and hold strategy
     buy_and_hold_btc = initial_cash / prices.iloc[0]
     buy_and_hold_value = buy_and_hold_btc * prices.iloc[-1]
-    
+
     print(f"\n{Colors.HEADER}{Colors.BOLD}------ Final Portfolio Performance ------{Colors.ENDC}")
     print(f"Initial Cash: ${initial_cash:.2f}")
     print(f"Final Portfolio Value: ${final_value:.2f}")

test_simulation.py

@@ -0,0 +1,42 @@
+import subprocess
+import sys
+import pandas as pd
+from bitcoin_trading_simulation import simulate_bitcoin_prices, calculate_moving_averages, generate_trading_signals
+
+
+def test_simulate_bitcoin_prices():
+    days = 10
+    prices = simulate_bitcoin_prices(days=days, initial_price=100, volatility=0.01)
+    assert len(prices) == days
+    assert isinstance(prices, pd.Series)
+    assert prices.iloc[0] == 100
+
+
+def test_calculate_moving_averages():
+    prices = pd.Series([100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110])
+    signals = calculate_moving_averages(prices, short_window=3, long_window=5)
+    assert 'short_mavg' in signals.columns
+    assert 'long_mavg' in signals.columns
+    assert not signals['short_mavg'].isnull().all()
+
+
+def test_generate_trading_signals():
+    # Create a dummy signal dataframe
+    data = {
+        'price': [100, 110, 100, 90, 100],
+        'short_mavg': [100, 110, 100, 90, 100],
+        'long_mavg': [100, 100, 100, 100, 100]
+    }
+    signals = pd.DataFrame(data)
+    signals = generate_trading_signals(signals)
+    assert 'positions' in signals.columns
+    # Check if signals are generated (1.0 or -1.0 or 0.0)
+    assert signals['signal'].isin([1.0, -1.0, 0.0]).all()
+
+
+def test_cli_execution():
+    # Test running the script via subprocess to ensure it doesn't crash
+    result = subprocess.run([sys.executable, 'bitcoin_trading_simulation.py'], capture_output=True, text=True)
+    assert result.returncode == 0
+    assert "Daily Trading Ledger" in result.stdout
+    assert "Final Portfolio Performance" in result.stdout